Shear modulus prediction of embedded pressurized salt layers and pinpointing zones at risk of casing collapse in oil and gas wells

Abstract

Rock shear and strain behaviors deep in the sub-surface are dependent on several geomechanical variables. These include various types of stress and rock formation moduli. Deformations of specific rock formations are usually quantified in a series of laboratory tests, which are costly and time-consuming and can only be performed on the few rock formation samples and cores available. Consequently, indirect estimates of geomechanical metrics from well-log data are required to measure variations throughout the entire length of a wellbore. Such measurements and detailed geomechanical models are required to identify zones at risk of casing collapse due to shear stress buildups. Such zones tend to occur in problematic ductile formations such as salt and swelling clays that are prone to creep. Machine-learning algorithms coupled with optimizers, and calibrated with a few mechanical rock test measurements, offer a practical method for predicting geomechanical characteristics along an entire wellbore. Hybrid models combining the optimizers, imperialist competitive (ICA) and gravitation search (GSA) algorithms, with the machine-learning algorithms, distance-weighted K-nearest neighbor (DWKNN) and multi-layer perceptron (MLP) are shown to achieve high prediction accuracy of shear modulus (Gs) from a large dataset (22,325 data records) crossing the problematic salt-rich Gachsaran formation in a wellbore from the Marun oil field (Iran). ICA-MLP, GSA-MLP, ICA-DWKNN and GSA-DWKNN models applied to four independent variables recorded by well logs were calibrated with some laboratory-derived Gs measurements. These models predict Gs with high levels of accuracy achieving coefficients of determination (R2) > 0.96. The GSA-DWKNN hybrid algorithm (R2 = 1) provided the highest prediction accuracy. Gs predictions can then be used to accurately identify zones at risk of casing collapse.